Adapting Vision Transformer for Efficient Change Detection

Understand

Most change detection models based on vision transformers currently follow a "pretraining then fine-tuning" strategy.

• This involves initializing the model weights using large scale classification datasets, which can be either natural images or remote sensing images.
• However, fully tuning such a model requires significant time and resources.
• In this paper, we propose an efficient tuning approach that involves freezing the parameters of the pretrained image encoder and introducing additional training parameters.